Course outcomes
| CO 1 To understand Bayesian decision theory and its use |
| CO 2 To gain knowledge about Bayesian estimation methods |
| CO3 To apply nonparametric techniques and linear discriminant functions |
| CO4 To gain knowledge about nonmetric methods and algorithm independent machine learning |
| CO5 To apply unsupervised learning and clustering |
Pattern recognition systems – the design cycle – learning and adaptation – Bayesian decision theory – continuous features – Minimum error rate classification – discriminant functions and decision surfaces – the normal density based discriminant functions. Bayesian parameter estimation – Gaussian case and general theory – problems of dimensionality – components analysis and discriminants- Nonparametric techniques – density estimation – Parzen windows – nearest neighborhood estimation – rules and metrics – decision trees – CART methods – algorithm-independent machine learning – bias and variance for regression and classification – resampling or estimating statistics- Unsupervised learning and clustering – mixture densities and identifiability – maximum likelihood estimates – application to normal mixtures – unsupervised Bayesian learning – data description and clustering – criterion functions for clustering – hierarchical clustering – k-means clustering.